SMART-HIRING - AN EXPLAINABLE END-TO-END PIPELINE FOR CV INFORMATION EXTRACTION AND JOB MATCHING

Sep 14, 2026

Hiring processes often require manual screening of hundreds of resumes per job, which consumes time and effort while introducing inconsistencies and human bias. This paper proposes Smart-Hiring, an end-to-end NLP pipeline that extracts structured CV information from unstructured text and semantically matches candidates to job descriptions. The approach combines document parsing, named-entity recognition, and contextual embeddings to represent resumes and jobs in a shared vector space. A modular, explainable design allows inspection of extracted entities and matching rationales, supporting transparent recruitment analytics. Experiments on real-world data across multiple domains show competitive accuracy with high interpretability, enabling scalable, fairness-aware hiring.

LanguageEnglish

CategoryGeneral

FormatPDF

Pages6

Reading Time2 min

AccessFree to download

Introduction

Motivation and challenges

Proposed approach and pipeline stages

Explainability and trustworthiness

Paper organization

Related Work

Resume information extraction

Job–candidate matching

arXiv :2511 .02537v 1 [ cs .CL] 4 Nov 2025 SMART-HIRING: AN EXPLAINABLE END-TO-END PIPELINE FOR CV INFORMATION EXTRACTION AND JOB MATCHING A PREPRINT Kenza KHELKHAL, Dihia LANASRI ATM Mobilis Algiers, Algeria [khelkhalkenza88@gmail.com](khelkhalkenza88@gmail.com) , ad [lanasri@esi.dz](lanasri@esi.dz) November 5, 2025 ABSTRACT Hiring processes often involve the manual screening of hundreds of resumes for each job, a task that is time and effort consuming, error-prone, and subject to human bias. This paper presents Smart-Hiring, an end-to-end Natural Language Processing (NLP) pipeline designed to automatically extract structured information from unstructured resumes and to semantically match candidates with job descriptions. The proposed system combines document parsing, named-entity recognition, and contextual text embedding techniques to capture skills, experience, and qualifications. Using advanced NLP technics, Smart-Hiring encodes both resumes and job descriptions in a shared vector space to compute similarity scores between candidates and job postings. The pipeline is modular and explainable, allowing users to inspect extracted entities and matching rationales. Experiments were conducted on a real-world dataset of resumes and job descriptions spanning multiple professional domains, demonstrating the robustness and feasibility of the proposed approach. The system achieves competitive matching accuracy while preserving a high degree of interpretability and transparency in its decision process. This work introduces a scalable and practical NLP framework for recruitment analytics and outlines promising directions for bias mitigation, fairness-aware modeling, and large-scale deployment of data-driven hiring solutions. Keywords Natural Language Processing · Resume Parsing · Job Matching · Explainable AI 1 Introduction Recruitment is a critical yet time-consuming process in modern organizations, often requiring human resources professionals to manually review hundreds of resumes for each job opening. This manual screening not only demands significant effort and time but also introduces inconsistencies and biases in candidate selection. The increasing availability of digital resumes and online job platforms has created an urgent need for automated, intelligent systems capable of efficiently analyzing candidate profiles while maintaining fairness, transparency, and interpretability in the decision-making process. Traditional approaches used for automatic or semi-automatic recruitment typically rely on keyword or rule-based matching, which are limited in their ability to capture the semantic relationships between job descriptions and candidate qualifications. For example, systems that depend on exact keyword matches may fail to recognize that “software developer” and “application engineer” represent semantically similar roles. Moreover, extracting information from unstructured resumes represent a challenging task. Most CVs are not respecting the ATS standard, identifying the right information in the correct position in each CV still a complex problem to resolve. Recent advances in Natural Language Processing (NLP) and deep learning—particularly with transformer-based language models—offer new opportunities for overcoming these limitations through context-aware understanding of textual information. To overcome these limits, we present Smart-Hiring, an end-to-end NLP pipeline designed to automate two fundamental stages of the recruitment process: (1) the extraction of structured information from heterogeneous and unstructured resumes, and (2) the semantic matching between candidate profiles and job descriptions. The system combines rulebased heuristics, lightweight machine learning models, and contextual text embeddings to achieve robust information extraction and semantically meaningful candidate-job alignment. Unlike conventional recruitment automation systems, Smart-Hiring emphasizes explaina

FAQs

What problem does Smart-Hiring address in recruitment workflows?

It targets the manual screening of many resumes, which is time-consuming, error-prone, and biased, by automating CV information extraction and candidate-job semantic matching.

How does Smart-Hiring perform resume parsing and job matching?

It uses document parsing and named-entity recognition to extract structured resume information, then contextual text embeddings encode resumes and job descriptions into a shared vector space to compute similarity scores.

What makes Smart-Hiring explainable for recruiters?

Beyond similarity scores, the system highlights the most influential factors behind each recommendation, such as matched skills, experience, or education, helping users inspect and audit automated decisions.

Sep 14, 2026

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